This question evaluates understanding of hyperparameter tuning methods, including the competencies to compare different search strategies, analyze trade-offs in efficiency and parallelization, and design experiments to optimize model performance.
Describe common methods for hyperparameter tuning in machine learning.
For each method, explain:
Include at least: manual search, grid search, random search, and more advanced methods such as Bayesian optimization or adaptive schemes.
Login required